Systems, methods, and devices for identifying and presenting identifications of significant attributes of unique items

ABSTRACT

The disclosure herein provides systems, methods, and devices for identifying and presenting identifications of significant attributes of unique items. A significant attributes system for identifying and presenting identifications of significant attributes of unique items comprises an item analysis engine, at least one driver models database, and a model building engine, wherein the item analysis engine comprises an item description receiver and one or more driver calculators.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/253,007, titled SYSTEMS, METHODS, AND DEVICES FOR IDENTIFYING ANDPRESENTING IDENTIFICATIONS OF SIGNIFICANT ATTRIBUTES OF UNIQUE ITEMS,filed Aug. 31, 2016, which is a continuation of U.S. patent applicationSer. No. 13/924,375, titled SYSTEMS, METHODS, AND DEVICES FORIDENTIFYING AND PRESENTING IDENTIFICATIONS OF SIGNIFICANT ATTRIBUTES OFUNIQUE ITEMS, filed Jun. 21, 2013, which claims the benefit of U.S.Provisional Application No. 61/774,477, titled SYSTEMS, METHODS, ANDDEVICES FOR IDENTIFYING SIGNIFICANT ATTRIBUTES OF UNIQUE ITEMS, filedMar. 7, 2013. Each of the foregoing applications is hereby incorporatedby reference herein in its entirety.

BACKGROUND Field

The disclosure relates generally to the field of identifying significantattributes of items, and more specifically to systems, methods, anddevices for identifying and presenting identifications of significantattributes of unique items.

Description

In considering the pricing of new products, the price often comprises adollar amount for the base product and one or more dollar amounts forany additional features or attributes of the product. For example, newcars are marketed with a window sticker detailing the dollar amount forthe base vehicle and for each of the options added to the vehicle. Fornew cars of the same make and model (for example, Ford F-150 truck), theadditive dollar values for additional features and attributes aretypically the same for each vehicle. When considering new vehicles ofdifferent models or different make and model, the additive dollaramounts are often still similar to one another. For example, the priceof a DVD player is relatively uniform across new pickup trucks fromdifferent manufacturers. These additive dollar amounts for new vehiclesoften reflect the manufacturer's cost, such as material, labor, andother manufacturing costs, plus some profit. Similarly, for new homes,the construction costs, such as materials and labor plus the cost ofadditional amenities, for example, appliances, etc., plus some profitoften determines the price for the home.

When items such as used cars and existing homes are considered forresale, each item is unique and will be priced in the context of thecurrent marketplace. Although the total price placed on such an item canoften still be attributed to the various features or attributes of theitem, the valuation placed on each individual feature or attribute willlikely be different. Accordingly, it can be advantageous to havesystems, methods, and devices for identifying and presenting significantattributes of unique items, customizable items, and/or items havingvarying conditions, such as used vehicles and homes.

SUMMARY

The disclosure herein provides systems, methods, and devices foridentifying and presenting identifications of significant attributes ofunique items, customizable items, and/or items having varyingconditions, such as used vehicles, homes, commercial real estate,household goods, collectibles, automotive components, and the like.

In some embodiments, a significant attributes system for identifying andpresenting identifications of significant attributes of unique itemscomprises: an item analysis engine configured to determine which of aplurality of attributes of a selected item are driver attributes, theitem analysis engine comprising: an item description receiver configuredto electronically receive item data, the item data being related to theplurality of attributes of the selected item; and one or more drivercalculators configured to apply one or more driver models to theplurality of attributes to identify which of the plurality of attributesof the selected item are driver attributes; wherein the item analysisengine is configured to electronically present the identification ofwhich of the plurality of attributes of the selected item are driverattributes; at least one driver models database configured toelectronically store information relating to the one or more drivermodels and to electronically communicate with the item analysis engine;and a model building engine configured to generate the one or moredriver models by applying one or more model specifications to datarelating to user activity, wherein the data relating to user activitycomprises logged interactions of users with a plurality of unique items.

In certain embodiments, a computer-implemented method for identifyingand presenting identifications of significant attributes of unique itemscomprises: logging, using a computer system, interactions of users witha plurality of unique items, wherein the logging compriseselectronically monitoring actions of the users interacting with one ormore item listing systems presenting for sale the plurality of uniqueitems; generating, using the computer system, one or more driver modelsby applying one or more model specifications to data relating to thelogged interactions of the users with the plurality of unique items;storing the one or more driver models in an electronic driver modelsdatabase; receiving, using the computer system, electronic item datarelating to a plurality of attributes of a selected item; applying,using the computer system, the one or more driver models stored in theelectronic driver models database to the plurality of attributes of theselected item to identify which of the plurality of attributes of theselected item are driver attributes; and presenting electronically theidentification of which of the plurality of attributes of the selecteditem are driver attributes; wherein the computer system comprises acomputer processor and electronic memory.

In some embodiments, a computer readable, non-transitory storage mediumhaving a computer program stored thereon for causing a suitablyprogrammed computer system to process by one or more processorscomputer-program code by performing a method for identifying andpresenting identifications of significant attributes of unique itemswhen the computer program is executed on the suitably programmedcomputer system comprises: logging, using a computer system,interactions of users with a plurality of unique items, wherein thelogging comprises electronically monitoring actions of the usersinteracting with one or more item listing systems presenting for salethe plurality of unique items; generating, using the computer system,one or more driver models by applying one or more model specificationsto data relating to the logged interactions of the users with theplurality of unique items; storing the one or more driver models in anelectronic driver models database; receiving, using the computer system,electronic item data relating to a plurality of attributes of a selecteditem; applying, using the computer system, the one or more driver modelsstored in the electronic driver models database to the plurality ofattributes of the selected item to identify which of the plurality ofattributes of the selected item are driver attributes; and presentingelectronically the identification of which of the plurality ofattributes of the selected item are driver attributes; wherein thecomputer system comprises a computer processor and electronic memory.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features, aspects, and advantages of the presentinvention are described in detail below with reference to the drawingsof various embodiments, which are intended to illustrate and not tolimit the invention. The drawings comprise the following figures inwhich:

FIG. 1 is an embodiment of a schematic diagram illustrating a useraccess point system.

FIG. 2 is a block diagram depicting an embodiment of a significantattributes system in communication with one or more other systems.

FIG. 3 depicts an embodiment of a process flow diagram illustrating anexample of applying one or more models to an item.

FIG. 4 depicts an example output of an embodiment of applying a pricedriver model to an item.

FIG. 5 depicts an embodiment of a process flow diagram illustrating anexample of a data collection and analysis process.

FIG. 6 depicts an embodiment of a process flow diagram illustrating anexample of building a model.

FIG. 7 depicts an embodiment of a process flow diagram illustrating anexample of identifying one or more driver attributes of a selected item.

FIG. 8 depicts an embodiment of a process flow diagram illustrating afurther example of identifying one or more driver attributes of aselected item.

FIG. 9 depicts an embodiment of a process flow diagram illustrating anexample of building one or more driver models.

FIG. 10 is a block diagram depicting an embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of the significant attributes and user access point systemsdescribed herein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe invention described herein extends beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinvention and obvious modifications and equivalents thereof. Embodimentsof the invention are described with reference to the accompanyingfigures, wherein like numerals refer to like elements throughout. Theterminology used in the description presented herein is not intended tobe interpreted in any limited or restrictive manner simply because it isbeing used in conjunction with a detailed description of certainspecific embodiments of the invention. In addition, embodiments of theinvention can comprise several novel features and no single feature issolely responsible for its desirable attributes or is essential topracticing the inventions herein described.

When items such as used cars and existing homes are considered forresale, each item is unique and will be priced in the context of thecurrent marketplace. The total price placed on such an item can often beattributed to the various features or attributes of the item. From aseller's perspective, the valuation placed on each individual feature orattribute will likely be the perceived utility or value of the featurein the mind of the seller, plus possibly some market considerations likescarcity, popularity, and other geographic factors. From the buyer'sperspective, the valuation of individual features will often differ fromthose of the seller, and from the original valuation placed on thefeatures by the manufacturer. Accordingly, it can be advantageous tohave systems, methods, and devices for identifying and presentingsignificant attributes of unique items, customizable items, and/or itemshaving varying conditions, such as used vehicles and homes.

The disclosure herein provides methods, systems, and devices to analyzea unique item, such as a used vehicle, to identify significant or driverattributes of the item. In an embodiment, a system can be configured togenerate price driver and/or demand driver models by logging andanalyzing interactions of users with electronic listings of variousitems for sale. The system can be configured to apply these price anddemand driver models to data relating to a selected item to identifywhich attributes of the selected item are price drivers and which aredemand drivers. In some embodiments, the system can be configured toelectronically receive a description of a selected item from a user orrequesting system and to return an identification of significant ordriver attributes of the selected item as described in further detailbelow.

In order to better explain the pricing of individual unique items, suchas used cars and existing homes, the disclosure herein provides methods,systems, and devices to identify those features that are contributing tothe price of the product, along with an estimate of the dollar amountattributable to each of these features. In some embodiments, thesesignificant features or attributes are called price drivers. Pricedrivers can be used to explain a price difference between two productsthat otherwise might look the same, or very similar. For example, avehicle might have high performance tires that are contributing to theoverall price of the vehicle, and identifying this fact can help toexplain a price difference between two vehicles that are similar exceptfor the tires. Price drivers and their associated dollar amounts can insome embodiments be used to implement a measure of vehicle similarity.For example, a system for measuring the similarity of two or more uniqueitems may be configured to consider the items to be more dissimilar asthe difference in value of their different features or attributesbecomes greater.

The disclosure herein also provides embodiments which identify thefeatures or attributes of an item that are contributing to the demandfor the item. In some embodiments, the significant features orattributes that contribute to the demand of a unique item are calleddemand drivers. Demand drivers can, for example, be used to explain whyone item is likely to sell faster than another item. For example,vehicles with leather seats may be in higher demand in a particularmarketplace, thereby resulting in a greater likelihood of being soldquickly, or at least sooner than a vehicle that has cloth seats. Thedemand influence can work in both directions. For example, a featurethat adds to the price of a product but is in low demand may result in alonger time to sell the product with this feature. For example, avehicle with heated seats might have less interest in the marketplacethan a vehicle without heated seats in a warm climate.

An initial sticker price or pricing breakdown of a new item may havelittle or no bearing on the price of the same item when it is offeredfor resale as a used item. Therefore, it is desirable to have systems,methods, and devices, to analyze unique items to determine the influencethat different significant attributes or features have to the pricingand/or demand of that item. In one embodiment, a price driver model isconstructed by analyzing historical used items offered for sale and anyuser interactions with the item listings. This model may, for example,account for the price of the base product and the incremental dollaramount for each of the features or attributes added to the product. Insome examples, it will be found from the historical data that somefeatures will not have any statistically significant correlation to thereselling price, and these features thus will not become price drivers.Such a price driver model may, in some embodiments, incorporate all orsome of the features or attributes of the item, in addition to featuresor attributes that characterize the geographic market conditions, suchas supply and demand for the item.

In some embodiments, a demand driver model is constructed that computesa relative performance factor, such as an expected conversion rate, of aunique item given the item's specific set of features and/or attributes.The relative performance factor may, for example, measure whether anitem will generate higher, normal, or lower buyer interest than isexpected of that type of item. The data used to construct the demanddriver model can comprise, for example, how often a user clicks on alink that takes the user to a detailed page about an item, the amount oftime a user spends on a page relating to an item, information submittedto an advertiser from a user, a purchase of an item, and/or the like.The demand driver model can be configured to incorporate all or some ofthe features or attributes of an item, in addition to features thatcharacterize the geographic market conditions, such as supply and demandfor an item.

In some embodiments, a significant attributes system is provided that isconfigure to accept a description of a unique item and to returninformation describing price and/or demand drivers for that item. Forexample, a user may submit to the system a description of a used vehicleoffered for sale, such as a Honda Accord with a certain number of milesand the various features or options the vehicle has. The description ofthe unique item may, in some embodiments, also include a description ofthe geographic location of the item, such as a zip code, city, state,region, etc. The system can be configured to apply price driver and/ordemand driver models to the description and return information to theuser such as, for example, the fact that this vehicle has leather seatsand a sun roof are important contributors to the price. The system maydetermine that, for example, the leather seats are contributingapproximately $500 to the price and the sun roof is contributingapproximately $200 to the price. This could allow the user to, forexample, account for this price difference when the user is comparingthe vehicle to a similar vehicle that does not have leather seats or asun roof.

With respect to demand drivers, the system can be configured to, forexample, report to a user the feature or features that are drivingdemand for the item. In this example, the system may determine that,while leather seats and a sun roof are significant contributors to theprice, the demand for the vehicle is primarily being driven by it havinga four cylinder engine instead of a six cylinder engine, because thefour cylinder engine saves gas. The system can be configured toadditionally determine features or options that are not price or demanddrivers. In the current example, if the Honda Accord has power locks,the system may determine that power locks are not a significant pricedriver and report to the user that the power locks option should not beadding much, if anything, to the price of the vehicle as compared to asimilar vehicle without power locks.

One challenge in determining demand drivers of a product or item isseparating any position bias from other factors influencing a conversionrate. Examining historical user activity indicates that there is aposition bias in user behavior, for example, that items shown higher ina sort order, such as on an automotive website listing used automobilesfor sale, the items shown higher in the sort order are more likely toresult in user conversion. Therefore, in a system that is analyzing thedemand drivers of an item, the system is better able to determine demanddrivers if the system can separate the position bias from other factorsinfluencing the conversion rate. For product categories where items areunique and/or have a high churn (such as when products frequently comeinto the current inventory and frequently are removed from the currentinventory as they are sold) a single item will often be viewed atvarious positions in search results, and hence will have a differentexpectation of conversion for each impression.

In some embodiments, a position bias model is generated and used tocapture how the position of an item, such as the order presented in thesearch results, impacts the chance that a user will select that item.There are many considerations that would impact the user's choice,including the various features or attributes of the item, and a positionbias model can be used to eliminate or at least partially eliminate theposition of the item when it is displayed to a user as one of thosefactors. With a position bias model combined with historical useractivity, such as impression and conversion counts, the systemsdescribed herein can be configured to anticipate the performance of anitem in search results relative to the expected conversion based on theposition at which the item was viewed by users in search results.

In some embodiments, one or more price driver and demand driver modelsare created and become the basis for a service that accepts adescription of a unique item and returns price driver and demand driverinformation related to that item. For example, the description of theunique item may describe a used car or an existing home for sale. Thedescription may also describe the geographic location of that item. Theservice can be configured to analyze the description of that unique itemand to apply the demand driver and/or price driver models to return alist of product features that are contributing to the price, an estimateof the dollar amounts of these contributions, and a list of featuresthat are in demand in the geographic location where the item is offeredfor sale.

Although various embodiments described herein are described withreference to used vehicles, the concepts described herein may beutilized to identify significant attributes for a variety of uniqueitems, customizable items, items having varying conditions, and evenservices. For example, the concepts described herein may be used toidentify significant attributes for real estate, existing homes,commercial real estate, household goods, customized electronics,customized goods, clothing, automotive components, collectibles,sporting goods, toys, hobby products, gift items, and/or various othertypes of unique or customizable products or items or products or itemshaving various conditions offered for sale.

As an example of the embodiments described herein being applied toservices offered for sale, a person offering a window washing servicemay be interested in determining which attributes of his or her serviceare most important to customers. For example, a system may be configuredto monitor and/or log listings or advertisements of services offered forsale, such as various competing window washing services. The system canbe configured to analyze data relating to historical sales and/orresponse rates of and/or user interactions with the various serviceadvertisements to develop one or more models that enable the system toidentify significant attributes of the services and/or theadvertisements listing the services. These techniques may also beapplied to various other services, such as dog walking services,computer repair services, car repair services, medical services,insurance services, and various other types of services.

FIG. 1 is an embodiment of a schematic diagram illustrating a useraccess point system 100. The user access point system 100 can, forexample, be configured to access a system as described herein, such asthe significant attributes system 202 shown in FIG. 2. The user accesspoint system 100 shown in FIG. 1 can be configured to, for example,accept information related to a unique item for sale, send thatinformation to a significant attributes system, receive information,such as price driver and demand driver information from the significantattributes system, and then display this information to a user of theuser access point system 100. The user access point system 100 comprisesan item identifier 102, a popularity indicator 104, price driverindicators 106, and demand driver indicators 108. The item identifier102 can be configured to display information describing the currentunique item the user is interested in. In this example, the user isinterested in a 2012 Chevrolet Cruise Echo. The popularity indicator 104can be configured to indicate, for example, the popularity of the modelof vehicle the user is currently interested in. In this example, thepopularity indicator 104 is indicating that a lot of people aresearching for this vehicle online right now. The information presentedby the popularity indicator 104 can be retrieved from, from example, asignificant attributes system, as shown in FIG. 2.

One or more price driver indicators 106 displayed by the user accesspoint system 100 are configured to indicate various price drivers of theitem selected by the user. In this example, the transmission type andexterior color type of the vehicle the user is interested in have beendetermined to be price drivers. The vehicle has an automatictransmission, which is estimated to add approximately $981.00 to thevalue of this used vehicle over the value of a base model. The exteriorcolor of this vehicle is white, which adds an estimated $205.00 to thevalue of a base model. The one or more demand driver indicators 108 areconfigured to indicate features or attributes of the current item thatare contributing significantly to its demand. For example, in this casethe item the user is interested in has a keyless entry feature. Thedemand driver indicator 108 indicates that this keyless entry feature isa popular feature that is in demand on automotive marketplaces acrossthe web. Although in this example the demand driver indicator 108indicates the feature is in demand across the web, in other examples oneor more demand drivers may be indicated as being in demand in certaingeographic regions, ZIP codes, etc.

The information displayed by the price driver indicators 106 and demanddriver indicators 108 can be obtained from, for example, the significantattributes system 202 shown in FIG. 2. The price driver and/or demanddriver information displayed by the user access point system 100 can beuseful to, for example, a user in the market for a used vehicle to helpdetermine what the user should pay for a particular used vehicle. Theinformation can also be useful to, for example, a user that is selling aused vehicle to help the seller determine which features are in demandand therefore which features the seller should emphasize to a potentialpurchaser.

FIG. 2 is a block diagram depicting an embodiment of a significantattributes system 202 in communication with one or more other systems.The significant attributes system 202 can be configured to acceptinformation describing a unique item and to return price driver and/ordemand driver information related to that unique item. The significantattributes system 202 can be configured to communicate with othersystems through, for example, a network 204. The network 204 maycomprise a local area network, a wide area network, the internet, acellular phone network, etc. The significant attributes system 202 canbe configured to communicate with, for example, one or more user accesspoint systems 100, such as the user access point system 100 shown inFIG. 1. The significant attributes system 202 can also be configured tocommunicate with various other systems. For example, a recommendationsystem 205 can be configured to recommend unique items to potentialpurchasers based on other unique items the potential purchaser hasexpressed interest in. The recommendation system 205 can be configuredto communicate with the significant attributes system 202 to retrieveestimated price driver and/or demand driver information from thesignificant attributes system 202 to assist in creating itsrecommendations. In another example, a time-on-market system 206 can beconfigured to communicate with the significant attributes system 202 toretrieve price driver and/or demand driver information related tovarious unique items to implement a system that estimates a time onmarket for a particular unique item. For example, a time-on-marketsystem 206 may be configured to consider a used vehicle for sale andretrieve price driver and/or demand driver information from thesignificant attributes system 202 to assist in estimating how long thatparticular used vehicle will be on the market before it is sold.

One or more user access point systems 100 can comprise an item selectionmodule or receiver 207 and a display module or interface 208. Thedisplay interface 208 can be configured to, for example, display thevarious features shown in the user access point system 100 of FIG. 1.The item selection receiver 207 can be configured to accept input orinformation from a user to, for example, indicate a unique item the useris interested in and to send that information to the significantattributes system 202 for determination of price drivers and/or demanddrivers.

The significant attributes system 202 comprises several systems anddatabases. The significant attributes system 202 comprises a datacollection system or engine 210, a model building engine 220, and anitem analysis system or engine 230. The significant attributes system202 further comprises a product description database or item attributesdatabase 240, a position bias models database 242, a price driver modelsdatabase 244, a demand driver models database 246, a market datadatabase 248, and a product activity database 250. The data collectionengine 210, model building engine 220, and item analysis engine 230 canbe configured to communicate with the various databases and othersystems to allow the significant attributes system 202 to acceptinformation describing unique items from another system and to returnprice driver and/or demand driver information relating to that uniqueitem.

The position bias models database 242 can be configured to containinformation describing one or more position bias models for use incollecting data on various conversion rates and other item-specificinformation and in building price driver and demand driver models, asfurther described below. The price driver models database 244 and demanddriver models database 246 can be configured to contain informationdescribing one or more price driver models and demand driver models,respectively. The price driver models and demand driver models can be,for example, generated by the model building engine 220, stored in thedatabases, and then applied by the item analysis engine 230 to calculatedemand drivers and price drivers of unique items.

The product description database 240 can be configured to containinformation describing current and/or historical items for sale. Forexample, the product description database 240 can be configured tocontain information on the current inventory of used vehicles for salein various markets. The product description database 240 can further beconfigured to contain information describing the various attributes orfeatures of the various items currently listed for sale and/or listedfor sale in the past. The information in the product descriptiondatabase 240 can be used, for example, by the data collection engine 210to analyze historical user activity and/or the model building engine 220to build price driver, demand driver, and/or position bias models, asfurther described below. In some embodiments, at least a portion of thedata stored in the product description database 240 can be provided byone or more electronic feeds from, for example, a vehicle dealer serviceprovider.

The market data database 248 can be configured to contain, for example,information describing current and/or historical product inventory anduser activity by geographic market. In some embodiments, the geographicmarket information can be organized by, for example, zip code,demographic marketing area, state, national region, and the like. Themarket data database 248 can be configured to be filled with informationand/or updated by the data collection engine 210 and then utilized bythe model building engine 220 to build price driver and demand drivermodels and by the item analysis engine 230 to generate price driver anddemand driver information to send to, for example, a user access pointsystem 100.

The product activity database 250 can be configured to contain, forexample, information describing user activity related to each uniqueitem described in, for example, the product description database 240. Insome embodiments, the user activity can include, for example, uniqueimpressions, clicks, leads, and the like. The product activity database250 can be configured to, for example, be filled with information and/orupdated by the data collection engine 210. In some embodiments, theinformation stored in the product activity database 250 can be utilizedby, for example, the model building engine 220 and/or the item analysisengine 230 in generating models and/or price driver and demand driverinformation, as further described below.

The data collection engine 210 comprises a user activity database 212, auser activity module or filter 214, a product activity module or filter216, and a market data module or filter 218. In some embodiments, thedata collection engine 210 can be configured to examine historicaland/or real time user activity or interactions and/or supply and demandinformation to help determine price drivers, demand drivers, andposition bias. The data collection engine 210 can be configured tocollect or log data describing user activities or interactions (forexample, impressions, clicks, leads, time spent on a webpage, etc.)from, for example, various internet product search sites or item listingservices, by using the user activity filter 214. In some embodiments,the data collection engine 210 is configured to collect or log the datasubstantially in real time. The user activity filter 214 can beconfigured to store this data in the user activity database 212. In someembodiments, the user activity database 212 is configured to store datadescribing at least 1,000, 10,000, 100,000, 1,000,000, 10,000,000 ormore user activities or interactions and/or data relating to 1,000,10,000, 100,000, 1,000,000, 10,000,000 or even over 18,000,000 itemlistings. The data collection engine 210 can be configured to generategeographic market data for storage in the market data database 248and/or product activity data for storage in the product activitydatabase 250 by combining the user activity information stored in theuser activity database 212 with the product description data stored inthe product description database 240 and a position bias model from theposition bias models database 242. This process is shown and furtherdescribed below with reference to FIG. 5.

The model building system or engine 220 comprises a specificationsdatabase 222, a specifications module or interface 224, and a trainingmodule or generator 226. The specifications database 222 can beconfigured to contain information describing various model buildingspecifications as defined by the specifications interface 224. Thespecifications interface 224 can be configured to accept instructionsfrom a user or administrator of the significant attributes system 202 todefine one or more model building specifications. In some embodiments,model building specifications may, for example, identify the explanatoryand response variables to be considered, the modeling approach, and/orthe product attributes that are candidates for price and/or demanddrivers. The training generator 226 can be configured to examine thehistorical or real time data generated by the data collection engine 210and stored in the market data database 248 and product activity database250, and to apply specifications from the specifications database 222 togenerate price driver models and/or demand driver models to be stored inthe price driver models database 244 and/or demand driver modelsdatabase 246. The training generator 226 can be configured to apply themodel construction or training techniques identified in the modelspecifications, examine the resulting model, and output a description ofprice drivers and/or demand drivers to be stored in the price driverand/or demand driver models databases. Price driver and demand drivermodels can then be configured to be accessed by, for example, the itemanalysis engine 230 to output price drivers and/or demand drivers for aparticular unique item. The training generator 226 can be configured usevarious training techniques, for example, linear regression, non-linearregression, model trees, nearest neighbor analysis, and/or the like. Insome embodiments, the model building engine 220 can be configured toupdate and/or regenerate price driver and/or demand driver modelssubstantially in real time based on newly logged user activity orinteraction data from the data collection engine 210.

The position bias models database 242 can be configured to store one ormore position bias models. In some embodiments, a position bias model isgenerated by examining historical data, such as product data withassociated user activity and/or geographic market data from the marketdata database 248 and/or the product activity database 250, to build amodel that characterizes any position bias. The position bias model canbe used to calculate an expected performance, such as an expected numberof conversions, of a unique product, given that the product was viewedin a particular position in a set of search results. In one embodiment,a position bias model is defined by the equation 1/N, where N is theposition of the product in the set of search results. This representsthe expected number of conversions relative to the number of conversionsthat occur for the product at position 1. In another embodiment, aposition bias model is defined by a log decay model. Given a startingvalue, a position, an additive adjustment, and an exponent, the decayvalue is calculated as follows:

decayValue=startValue;

for (i=2; i<=n; i++) {

-   -   decayValue *=(1.0−Math.pow(1.0/(i+additiveAdjustment),        exponent));

}

The item analysis engine 230 comprises an item description module orreceiver 232, a price driver module or calculator 234, and a demanddriver module or calculator 236. The item analysis engine 230 can beconfigured to accept a unique item's description, using the itemdescription receiver 232, and then to generate price and demand driversfor that item. The item analysis engine 230 can be configured to usegeographic market data from the market data database 248 and price anddemand driver models from the price driver models database 244 and thedemand driver models database 246, and to apply that information togenerate price drivers and demand drivers using the price drivercalculator 234 and demand driver calculator 236. The application ofthese models is described in more detail below with reference to FIG. 3.

In some embodiments, the data collection engine 210 operatessubstantially in real time by logging user interactions with variousunique items as the users are interacting with the listings of theseunique items. One or more computer systems is necessary for the datacollection process due at least in part to the volume of informationrequired to be collected to enable the data collection engine 210 togenerate useful data for use by the model building engine 220. A humanwould not realistically be able to monitor one or more or a multitude ofitem listing systems substantially in real time, as numerous users aresimultaneously interacting with listings of these services. In someembodiments, the data collection engine 210 may comprise 5, 10, 50, 100or more item listing services or systems that all need to be monitoredsubstantially in real time and substantially simultaneously. In someembodiments, each of the item listing systems may have 5, 10, 50, 100,1000 or more users using the listing system substantiallysimultaneously, adding to the need for at least one computer system tomonitor the interactions of users with listings.

In some embodiments, other portions of the significant attributes system202 also operate substantially in real time. For example, when a user ofthe significant attributes system 202 selects an item the user isinterested in identifying driver attributes of, such as by using theuser access point system 100, the user access point system 100 isconfigured to send data relating to the selected item to the significantattributes system 202 through the network 204. The user of the useraccess point system 100 will expect a response from the significantattributes system 202 in a relatively short amount of time. The usermay, for example, expect an identification of driver attributes from thesignificant attributes system in merely the length of time a webpagetakes to load. In some instances, the time available to identifysignificant attributes of a selected item may comprise a few seconds oreven less time, such as less than one second. Therefore, a significantattributes system configured to identify significant attributes of aselected item requires at least one computer system configured toidentify the significant or driver attributes substantially in realtime. A human would not be able to analyze a selected item's attributes,apply price and demand driver models to the attributes, and present theidentified driver attributes all in a manner of seconds or even lesstime. Rather, if a human were even able to perform these tasks, thehuman would spend several orders of magnitude more time on the process,which would be unacceptable to most users of such a system.

Not only is one or more computer systems and/or computer hardwarerequired to operate the data collection engine 210 and/or other portionsof the significant attributes system 202 to allow the system to operateat an acceptable speed, but a human would not even be able to perform atleast some of the operations performed by the significant attributessystem 202. For example, the data collection engine 210 in someembodiments requires simultaneous monitoring of multiple item listingservices generating websites for display to a multitude of users. Ahuman being would not be able to realistically monitor all of theseinteractions without the assistance of a computer system. With respectto other portions of the significant attributes system 202, variouscalculations take place that would be extremely complex for a human todo without the assistance of a computer system. Some examples are thecalculations required to generate and apply price and demand drivermodels.

Additionally, when operating a significant attributes system, amultitude of variables must be tracked. For example, the model buildingengine 220 may take into account 10, 50, 100, 1000, 10,000, or moreunique items in the calculation and building of the price and demanddriver models. In addition to the amount of time it would take a humanto perform such calculations, it would be difficult, if not impossible,for a human to keep track of all of the variables required for suchcalculations. Therefore, it can be seen that the operation of asignificant attributes system as described herein necessitates the useof computer hardware and/or at least one computer system.

FIG. 3 depicts an embodiment of a process flow diagram illustrating anexample of applying one or more price driver and/or demand driver modelsto an item, such as a unique item. In some embodiments, the processshown in FIG. 3 can be performed by, for example, the significantattributes system 202 shown in FIG. 2, and more specifically the itemanalysis engine 230 of the significant attributes system 202. At block302 a product description is provided. The product description may insome embodiments comprise a unique identifier of an item. For example,the product description may comprise a unique identifier that identifiesa specific item for sale in current inventory. In some embodiments, theunique identifier may identify information stored in the productdescription database 240 that describes the item's attributes andfeatures. In some embodiments, the product description comprises, ratherthan, or in addition to a unique identifier, information describing aunique item, such as information describing its various attributes andfeatures. For example, the product description may comprise a listing ofyear, model, body style, trim, and/or other options and features of aspecific used vehicle.

At block 304 geographic market data is provided. For example, the itemanalysis engine 230 of the significant attributes system 202 receivesinformation from a user or another system describing where the productdescribed in the product description at block 302 is being offered forsale. This information may comprise, for example, a zip code, ademographic marketing area, a state, a national region, and the like. Insome embodiments, a user of a significant attributes system 202 mayprovide the geographic market data along with the product description.In other embodiments, a user provides only the product description, andthe item analysis engine 230 retrieves the geographic market data fromthe market data database 248 and/or the product description database240, based on the product description and/or unique identifier providedby the user.

At block 306 the product description and geographic market data aremerged. For example, the geographic market data can be associated withthe product description information. At block 308 a price driver modelis provided. For example, the item analysis engine 230 may be configuredto retrieve a price driver model from the price driver models database244. At block 310 a demand driver model is provided. For example, theitem analysis engine 230 may be configured to retrieve a demand drivermodel from the demand driver models database 246.

At block 312 the price driver model and demand driver model are applied.For example, the price driver calculator 234 of the item analysis engine230 may be configured to apply the price driver model provided at block308 to the product description and/or geographic market data provided atblocks 302 and 304. The demand driver calculator 236 of the itemanalysis engine 230 can be configured to apply the demand driver modelprovided at block 302 to the product description and/or geographicmarket data provided at blocks 302 and 304. At block 314 the pricedrivers and demand drivers are output from the item analysis engine 230.For example, FIG. 4 depicts an example output of an embodiment ofapplying a price driver model to an item. As shown in FIG. 4, four pricedrivers are illustrated based on a product description describing aChevrolet Colorado for sale in North Carolina. In this example, the fourfeatures or attributes 402 of this product that are determined to beprice drivers are that the vehicle has an extended cab, that the vehiclehas a four wheel drive option, that the vehicle has a towing package,and that the vehicle is a certified pre-owned vehicle. Column 404 of thetable shown in FIG. 4 indicates the anticipated value the item analysisengine 230 determined each of these features or attributes contributesor adds to the base price of a Chevrolet Colorado for sale in NorthCarolina. In this example, the extended cab option is anticipated to add$2,260.00. The four wheel drive option is estimated to add $2,390.00.The towing package option is estimated to add $940.00. The certifiedpre-owned option is estimated to add $670.00. Although the embodimentshown in FIG. 4 illustrates the price driver information in a tableformat, the price driver information may in other embodiments bedisplayed to a user in various ways and/or sent to another system invarious formats. For example, the information may be displayed using auser access point system 100 as shown in FIG. 1. In other embodiments,the price driver information may be sent to another system, such as therecommendation system 205 or time-on-market system 206 shown in FIG. 2for use within that system.

FIG. 5 depicts an embodiment of a process flow diagram illustrating anexample of a data collection and analysis process. At blocks 502 and 504various users interact with various product search sites. For example,the product search sites may comprise various internet websites offeringused vehicles for sale and showing used vehicle listings. The productsearch sites can be configured to track and/or record various useractivities when the users are interacting with the search sites and/orlistings of various vehicles. For example, the product search sites canbe configured to allow users to interact with the sites by searching forlistings, clicking on listings, comparing listings to other listings,indicating an interest in a listing, etc. The search sites can beconfigured to track or record these interactions and to associate thetracked or recorded interactions with one or more specific vehiclelisting. In some embodiments, the tracking or recording of user activityis performed by a user activity filter, such as the user activity filter214 of the data collection engine 210 shown in FIG. 2.

At blocks 506 user activity logs are generated. In some embodiments theuser activity logs are generated by the various product search sites. Insome embodiments the user activity logs are generated by a user activityfilter, such as the user activity filter 214 of the data collectionengine 210 shown in FIG. 2. The user activity logs may, for example,comprise information describing how users interacted with variouslistings, such as used vehicle listings, including the tracked orrecorded information described above. The user activity logs may, forexample, list which vehicle listings users viewed, how long users viewedeach listing, where the user clicked within each listing, whether userscompared certain listings to other listings, whether a user requestedmore information on certain listings, etc. The user activity logs insome embodiments can be configured to associate the user activity withspecific listings, such as by using a unique item identifier.

At block 508 the various user activity logs are merged together. Forexample, the user activity filter 214 of the data collection engine 210can be configured to merge the various user activity logs into onelarger user activity log and to store this information in the useractivity database 212. Merging the user activity logs may comprise, forexample, combining the tracked activity of various users for eachindividual unique item, because each individual unique item will oftenbe interacted with by more than one user. The merged user activity logsare provided at block 510.

At block 512 product description data is provided. For example, theproduct description data may comprise the various attributes and/orfeatures of various products listed on the market for sale orhistorically listed on the market for sale. The product description datacan be provided by, for example, the product description database 240 ofthe significant attributes system 202 shown in FIG. 2. At block 514 aposition bias model is provided. The position bias model can be providedby, for example, the position bias models database 242 of thesignificant attributes system 202.

At block 516 the provided product description data, merged user activitylogs, and position bias model are combined and analyzed to determine arelative performance of each item that was viewed by one or more users.The output of this combination may, for example, result in product datawith user activity data being output at block 518 and geographic marketdata being output at block 520. In some embodiments, the productactivity filter 216 of the data collection engine 210 of FIG. 2generates the product data with user activity data and stores this datain the product activity database 250. The market data filter 218 of thedata collection engine 210 can be configured to generate the geographicmarket data and to store this data in the market data database 248. Theproduct data with user activity data output at block 518 may comprise,for example, a description of the user activity for each unique item.This may comprise, for example, unique impressions, clicks, leads, andthe like. The geographic market data provided at block 520 may comprise,for example, a description of product inventory and user activity bygeographic market. As discussed above, the geographic market may bedefined by zip code, demographic marketing area, state, national region,and the like.

FIG. 6 depicts an embodiment of a process flow diagram illustrating anexample of building a model, such as a price driver or demand drivermodel. The process shown in FIG. 6 may be performed by, for example, themodel building engine 220 shown in FIG. 2. The process illustrated inFIG. 6 may be used to generate, for example, price driver models and/ordemand driver models to be stored in the price driver models database244 and/or the demand driver models database 246 of the significantattributes system 202 shown in FIG. 2. At block 602 product data withuser activity data is provided to, for example, the model buildingengine 220. The product data with user activity data may comprise, forexample, the product data with user activity data output at block 518 ofthe process shown in FIG. 5. In some embodiments, the product data withuser activity data comprises a description of user activity for eachunique item, for example, unique impressions, clicks, leads, and thelike. The product data with user activity data may be provided by, forexample, the product activity database 250. At block 604 geographicmarket data is provided to, for example, the model building engine 220.The geographic market data may be, for example, the geographic marketdata output at block 520 of the process shown in FIG. 5. The geographicmarket data may be provided by, for example, the market data database248 of the significant attributes system 202 shown in FIG. 2.

At block 606 a model specification is provided. In some embodiments, themodel specification may be retrieved from, for example, thespecifications database 222 of the model building engine 220. The modelspecification may comprise, for example, an identification of theexplanatory and response variables to be considered, the modelingapproach, and the product attributes that are candidates for priceand/or demand drivers. At block 608 the price driver model and/or demanddriver model are generated. For example, the training generator 226 ofthe model building engine 220 applies the model specification providedat block 606 to the product data with user activity data and geographicmarket data provided at blocks 602 and 604 to generate price driverand/or demand driver models. The training generator 226 may use variousmodel training techniques to generate these models. For example, thetraining generator 226 may use linear regression, non-linear regression,model trees, nearest neighbor analysis, and the like.

At block 610 the model or models are output. The price driver model maybe stored in, for example, the price driver models database 244 of thesignificant attributes system 202. The demand driver model may be storedin, for example, the demand driver models database 246 of thesignificant attributes system 202. The price driver and demand drivermodels are stored in their respective databases for use at a later timeby, for example, the item analysis engine 230 of the significantattributes system 202 to generate price driver and demand driverinformation related to a specific unique item as shown and described inFIG. 3.

FIG. 7 depicts an embodiment of a process flow diagram illustrating anexample of identifying one or more driver attributes of a selected item.The process shown in FIG. 7 may be performed by, for example, thesystems shown in FIG. 2. At block 702 a user or requesting system startsthe process. At block 704, the user or requesting system selects anitem. For example, a user may use a user access point system, such asthe user access point system 100 shown in FIG. 2, to select a uniqueitem, such as a used vehicle currently listed for sale or a used vehiclethat the user intends to list for sale. At block 706, a significantattributes system receives details of the selected item. For example,the significant attributes system 202 shown in FIG. 2 may receivedetails of the selected item through the network 204 from, for example,the user access point system 100 or another system, such as arecommendation system 205 or time on market system 206. The selecteditem details may comprise, for example, attributes of the selected itemand/or geographic market data, such as where the item is listed forsale.

At block 708, an item analysis engine receives a price driver model. Forexample, the item analysis engine 230 shown in FIG. 2 may be configuredto retrieve one or more price driver models from the price driver modelsdatabase 244 of the significant attributes system 202. At block 710, theitem analysis engine receives a demand driver model. For example, theitem analysis engine 230 of the significant attributes system 202 may beconfigured to retrieve one or more demand driver models from the demanddriver models database 246.

At block 712, a price driver calculator applies the price driver modelto the details of the selected item to identify price driver attributesof the selected item. For example, the price driver calculator 234 ofthe item analysis engine 230 may be configured to apply the price drivermodel retrieved at block 708 to the details of the selected itemreceived at block 706. At block 714 a demand driver calculator applies ademand driver model to the details of the selected item to identifydemand driver attributes of the selected item. For example, the demanddriver calculator 236 of the item analysis engine 230 may be configuredto apply the demand driver retrieved at block 710 to the details of theselected item received at block 706.

At block 716, the significant attributes system presents the identifieddriver attributes. For example, the significant attributes system 202may compile the calculated demand drivers and price drivers from blocks712 and 714 and present those drivers through, for example, a network,such as the network 204 shown in FIG. 2. At block 718, the user orrequesting system displays the presentation and/or forwards thepresentation to another system. For example, the user access pointsystem 100 may display the calculated demand and/or price driverattributes to a user using the display interface 208. In anotherexample, a recommendation system, such as the recommendation system 205,may utilize the demand driver and/or price driver attributes ingenerating a recommendation of alternative items to a user of therecommendation system.

FIG. 8 depicts an embodiment of a process flow diagram illustrating afurther example of identifying one or more driver attributes of aselected item. The process shown in FIG. 8 may be implemented by, forexample, the systems shown in FIG. 2. At block 802, a user or requestingsystem starts the process. At block 804, the user or requesting systemselects an item. For example, a user may select an item using the useraccess point system 100 shown in FIG. 2. The user may use the itemselection receiver 207 to indicate a selection of an item that is, forexample, currently listed for sale or that the user may be interested inlisting for sale. For example, a user that is going to sell his or herused automobile may select that used automobile as the selected item,because the user is interested in determining which attributes of theautomobile are price driver and/or demand driver attributes.

At block 806, the user or requesting system sends data indicating theselected item to a significant attributes system, such as thesignificant attributes system 202 shown in FIG. 2. In some embodiments,the user or requesting system sends data that incorporates variousattributes of the selected item. These various attributes may comprise,for example, the item's year, model, body style, trim, and/or otheroptions and features of the item. The data may in some embodimentscomprise geographic market data, such as where the item is being offeredfor sale. In some embodiments, the data sent by the user or requestingsystem comprises a unique identifier associated with the selected item.In some embodiments, the unique identifier is sent along with other datadescribing attributes of the item. In other embodiments, the user orrequesting system only sends the unique identifier and does not sendadditional data describing the item, since the significant attributessystem may be able to retrieve the other data based on the uniqueidentifier.

At block 808, the significant attributes system receives the dataindicating the selected item. In some embodiments, as described above,this data may be a unique identifier, and/or may include variousinformation relating to attributes of the selected item and/orgeographic market data. At block 810, the significant attributes systemdetermines whether attributes of the selected item need to be retrieved.For example, if data related to the attributes of the selected item werenot included in the data sent from the user or requesting system, thesignificant attributes system may retrieve that data. If attribute dataneeds to be retrieved, the process moves to block 812. At block 812, thesignificant attributes system retrieves attributes relating to theselected item from an item attributes database. For example, thesignificant attributes system may use a unique identifier received fromthe user or requesting system to retrieve item attribute data from anitem attributes database shown at block 814. The item attributesdatabase may be, for example, the item attributes database or productdescription database 240 shown in FIG. 2.

Once the item attribute information has been retrieved, or if additionalattributes do not need to be retrieved, the process moves to block 816.At block 816, the significant attributes system determines whethermarket data needs to be retrieved. For example, if the user orrequesting system did not send data to the significant attributes systemindicating where the selected item is listed for sale or will be listedfor sale, the significant attributes system can be configured toretrieve this information from a database. If market data needs to beretrieved, the process moves to block 818. At block 818, the significantattributes system retrieves market data from a market data database. Forexample, the significant attributes system may utilize a uniqueidentifier sent by the user or requesting system to access geographicmarket data related to the selected item in a market data database shownat block 820. The market data database shown at block 820 may be, forexample, the market data database 248 shown in FIG. 2.

After market data has been retrieved, or if market data does not need tobe retrieved, the process moves to block 822. At block 822, an itemanalysis engine merges the item attribute data with the geographicmarket data. In some embodiments, the item attribute data may already bemerged with the geographic market data, such as when the user orrequesting system sends this information to the significant attributessystem, instead of sending a unique identifier to the significantattributes system. In other embodiments, however, such as when the itemattributes and geographic market data have been retrieved from the itemattributes database and market data database, the item analysis enginemay need to merge this information prior to applying any price or demanddriver models. In merging the data, for example, the geographic marketdata can be associated with item attribute data or product descriptioninformation.

At block 824, a price driver calculator retrieves a price driver model.For example, the price driver calculator 234 of the item analysis engine230 shown in FIG. 2 can be configured to retrieve a price driver modelfrom a price driver models database shown at block 826. The price drivermodels database shown at block 826 can be, for example, the price drivermodels database 244 shown in FIG. 2. At block 828, a demand drivercalculator retrieves a demand driver model. For example, the demanddriver calculator 236 shown in FIG. 2 can be configured to retrieve ademand driver model from a demand driver models database shown at block830. The demand driver models database shown at block 830 can be, forexample, the demand driver models database 246 shown in FIG. 2.

At block 832, the price driver calculator applies the retrieved pricedriver model to the merged item attribute data and geographic marketdata to identify price driver attributes of the selected item. At block834, the demand driver calculator applies the retrieved demand drivermodel to the merged item attribute data and geographic market data toidentify demand driver attributes of the selected item. Application ofthe price driver and demand driver models can be performed asillustrated in described above with reference to FIG. 3.

At block 836, the significant attributes system determines which driverattributes to present. For example, the significant attributes systemmay determine to present only a subset of the identified price and/ordemand drivers. In some embodiments, the significant attributes systemcan be configured to present all of the identified price and demanddriver attributes. In other embodiments, the significant attributessystem can be configured to present, for example, only the mostsignificant price and demand driver attributes. In some embodiments, thesignificant attributes system can be configured to accept a selectionfrom the user or requesting system that indicates which or how manydriver attributes to present. In some embodiments, an administrator ofthe significant attributes system can configure which or how many driverattributes to present.

At block 838, the significant attributes system presents the driverattributes. For example, the significant attributes system can beconfigured to send data identifying the price and/or demand driverattributes through a network, such as the network 204 shown in FIG. 2.At block 840, the user or requesting system receives the presentation.For example, the user access point system 100 shown in FIG. 2 mayreceive the presentation from the significant attributes system throughthe network 204. At block 842, the user or requesting system displaysthe presentation and/or forwards the presentation to another system. Forexample, the user access point system 100 may electronically display thedriver attributes to a user using the display interface 208 shown inFIG. 2.

FIG. 9 depicts an embodiment of a process flow diagram illustrating anexample of building one or more driver models. The process shown in FIG.9 may be performed by, for example, the significant attributes system202 shown in FIG. 2, and more specifically, the data collection engine210 and model building engine 220. The price and/or demand driver modelsgenerated using the process shown in FIG. 9 may be utilized by asignificant attributes system to identify price and/or demand drivers ofselected items, such as by the processes illustrated in FIGS. 3 and 8.

At block 902 users interact with item listings. For example, one or moreitem listing systems may list items for sale and allow users to interactwith these listings. In one embodiment, various used automobile websiteslist various used automobiles for sale and allow users of each of thosewebsites to interact with the listings, such as by clicking on listings,comparing one listing to another, expressing interest in a listing,purchasing an item that is the subject of a listing, and/or the like. Insome embodiments, the item listing systems are part of a significantattributes system, such as the significant attributes system 202 shownin FIG. 2. In other embodiments, the item listing systems are separatefrom the significant attributes system. In some embodiments, the processshown in FIG. 9 may involve users interacting with 1, 10, 100, 1000, ormore item listing systems.

At block 904 the item listing systems log details of the userinteractions. For example, the various item listing systems store datadescribing or relating to the various user interactions from block 902in a database. In some embodiments, the item listing systems store thisdata in a user activity database shown at block 906. The user activitydatabase shown at block 906 may be, for example, the user activitydatabase 212 of the data collection engine 210 shown in FIG. 2. In someembodiments, the item listing systems log user interactionssubstantially in real time. Logging user interactions substantially inreal time may be advantageous to enable relatively quick creation and orupdating of price driver models and/or demand driver models in responseto user interactions with listings. Logging interactions in real timemay also be important, because the item listing systems may beconfigured to be available substantially 24 hours a day, and a delay inlogging user interactions would likely not realistically allow thelogging of all or a significant number of the user interactions.

At block 908, a data collection engine merges user activity logs fromthe various listing systems. For example, the data collection engine 210shown in FIG. 2 may be configured to retrieve the various user activitylogs created by the item listing systems from the user activity databaseshown at block 906. The data collection engine can be configured to thenmerge these user activity logs into a single activity log for lateranalysis. At block 910, the data collection engine retrieves itemattribute data for items related to the activity logs. For example, thedata collection engine may be configured to access an item attributesdatabase shown at block 912 to retrieve attribute data describing thevarious items that users interacted with as shown in the activity logs.The item attributes database shown at block 912 may be, for example, theitem attributes database or product description database 240 shown inFIG. 2. In some embodiments, the user activity logs and/or the mergeduser activity log already contains item attribute data for the variousitems related to the logs, and block 910 is not required.

At block 914, the data collection engine retrieves a position biasmodel. The data collection engine may be configured to retrieve theposition bias model from, for example, a position bias models databaseshown at block 916. The position bias models database shown at block 916may be, for example, the position bias models database 242 shown in FIG.2. As described above, utilizing a position bias model in generatingprice and/or demand driver models may be advantageous, because userinteractions with item listings may be influenced by the position inwhich an item is displayed to the user among a list of other items.

At block 918, a product activity filter analyzes the logged data togenerate product data with associated user activity data. For example,the product activity filter 216 shown in FIG. 2 may be configured tocombine the merged user activity log with the product description dataretrieved at block 910 and the position bias model retrieved at block914 to determine a relative performance of each item that was viewed byone or more users. At block 920, the product activity filter stores thegenerated product data with user activity data in a product activitydatabase shown at block 922. The product activity database 922 may be,for example, the product activity database 250 shown in FIG. 2.

At block 924, a market data filter analyzes the logged data to generategeographic market data. For example, the market data filter 218 shown inFIG. 2 may be configured to combine the merged user activity log withthe product description data retrieved at block 910 and the positionbias model retrieved at block 914 and analyze this information togenerate geographic market data. At block 926, the market data filterstores the geographic market data in a market data database shown atblock 928. The market data database shown at block 928 may be, forexample, the market data database 248 shown in FIG. 2.

Beginning at block 930, a model building engine is configured togenerate price driver and demand driver models based on the productactivity and market data generated in previous blocks. At block 930, aspecifications interface retrieves a price driver model specification.The price driver model specification may be retrieved from, for example,a specifications database shown at block 932. The specificationsdatabase shown at block 932 may be, for example, the specificationsdatabase 222 of the model building engine 220 shown in FIG. 2. The modelspecification may comprise, for example, an identification of theexplanatory and response variables to be considered, the modelingapproach, and the product attributes that are candidates for pricedrivers.

At block 934, a training generator applies the price driver modelspecification to the previously stored product activity and market datato create a price driver model. For example, the training generator 226shown in FIG. 2 may be configured to retrieve the product data with useractivity data from the product activity database shown at block 922 andthe geographic market data shown at block 928 and to apply the pricedriver model specification to this data to generate a price drivermodel. At block 936, the training generator stores the price drivermodel in a price driver models database shown at block 938. The pricedriver models database shown at block 938 may be, for example, the pricedriver models database 244 shown in FIG. 2.

At block 940, the specifications interface retrieves a demand drivermodel specification. The specifications interface may be configured toretrieve the demand driver model specification from the specificationsdatabase shown at block 932. The demand driver model specification maycomprise, for example, an identification of the explanatory and responsevariables to be considered, the modeling approach, and the productattributes that are candidates for demand drivers.

At block 942, the training generator applies the demand driver modelspecification to the previously stored product and market data to createa demand driver model. For example, the training generator may beconfigured to retrieve the product data with user activity data storedin the product activity database shown at block 922 and the geographicmarket data stored in the market data database shown at block 928 and toapply the demand driver model specification to this data to create thedemand driver model. At block 944, the training generator stores thedemand driver model in a demand driver models database shown at block946. The demand driver models database shown at block 946 may be, forexample, the demand driver models database 246 shown in FIG. 2. Theprice driver and demand driver models stored at blocks 936 and 944 maybe configured to be used by a significant attributes system to determineor identify price and/or demand drivers, such as is shown and describedin relation to FIGS. 3 and 8.

Computing System

FIG. 10 is a block diagram depicting an embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of the significant attributes systems described herein.

In some embodiments, the computer clients and/or servers described abovetake the form of a computing system 1000 illustrated in FIG. 10, whichis a block diagram of one embodiment of a computing system that is incommunication with one or more computing systems 1017 and/or one or moredata sources 1019 via one or more networks 1016. The computing system1000 may be used to implement one or more of the systems and methodsdescribed herein. In addition, in one embodiment, the computing system1000 may be configured to manage access or administer a softwareapplication. While FIG. 10 illustrates one embodiment of a computingsystem 1000, it is recognized that the functionality provided for in thecomponents and modules of computing system 1000 may be combined intofewer components and modules or further separated into additionalcomponents and modules.

Significant Attributes System Module

In one embodiment, the computing system 1000 comprises a significantattributes system module 1006 that carries out the functions describedherein with reference to identifying significant attributes of uniqueitems, including any one of techniques described above. In someembodiments, the computing system 1000 additionally comprises a useractivity filter, product activity filter, market data filter,specifications interface, training generator, item description receiver,price driver calculator, demand driver calculator, item selectionreceiver, and/or display interface that carries out the functionsdescribed herein with reference to identifying significant attributes.The significant attributes system module 1006 and/or other modules maybe executed on the computing system 1000 by a central processing unit1002 discussed further below.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, COBOL, CICS, Java, Lua, C or C++. Asoftware module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an EPROM. It will be further appreciated that hardwaremodules may be comprised of connected logic units, such as gates andflip-flops, and/or may be comprised of programmable units, such asprogrammable gate arrays or processors. The modules described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

Computing System Components

In one embodiment, the computing system 1000 also comprises a mainframecomputer suitable for controlling and/or communicating with largedatabases, performing high volume transaction processing, and generatingreports from large databases. The computing system 1000 also comprises acentral processing unit (“CPU”) 1002, which may comprise a conventionalmicroprocessor. The computing system 1000 further comprises a memory1004, such as random access memory (“RAM”) for temporary storage ofinformation and/or a read only memory (“ROM”) for permanent storage ofinformation, and a mass storage device 1008, such as a hard drive,diskette, or optical media storage device. Typically, the modules of thecomputing system 1000 are connected to the computer using a standardsbased bus system. In different embodiments, the standards based bussystem could be Peripheral Component Interconnect (PCI), Microchannel,SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA)architectures, for example.

The computing system 1000 comprises one or more commonly availableinput/output (I/O) devices and interfaces 1012, such as a keyboard,mouse, touchpad, and printer. In one embodiment, the I/O devices andinterfaces 1012 comprise one or more display devices, such as a monitor,that allows the visual presentation of data to a user. Moreparticularly, a display device provides for the presentation of GUIs,application software data, and multimedia presentations, for example. Inone or more embodiments, the I/O devices and interfaces 1012 comprise amicrophone and/or motion sensor that allow a user to generate input tothe computing system 1000 using sounds, voice, motion, gestures, or thelike. In the embodiment of FIG. 10, the I/O devices and interfaces 1012also provide a communications interface to various external devices. Thecomputing system 1000 may also comprise one or more multimedia devices1010, such as speakers, video cards, graphics accelerators, andmicrophones, for example.

Computing System Device/Operating System

The computing system 1000 may run on a variety of computing devices,such as, for example, a server, a Windows server, a Structure QueryLanguage server, a Unix server, a personal computer, a mainframecomputer, a laptop computer, a tablet computer, a cell phone, asmartphone, a personal digital assistant, a kiosk, an audio player, ane-reader device, and so forth. The computing system 1000 is generallycontrolled and coordinated by operating system software, such as z/OS,Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, WindowsVista, Windows 7, Windows 8, Linux, BSD, SunOS, Solaris, Android, iOS,BlackBerry OS, or other compatible operating systems. In Macintoshsystems, the operating system may be any available operating system,such as MAC OS X. In other embodiments, the computing system 1000 may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, and I/O services,and provide a user interface, such as a graphical user interface(“GUI”), among other things.

Network

In the embodiment of FIG. 10, the computing system 1000 is coupled to anetwork 1016, such as a LAN, WAN, or the Internet, for example, via awired, wireless, or combination of wired and wireless, communicationlink 1014. The network 1016 communicates with various computing devicesand/or other electronic devices via wired or wireless communicationlinks. In the embodiment of FIG. 10, the network 1016 is communicatingwith one or more computing systems 1017 and/or one or more data sources1019.

Access to the significant attributes system module 1006 of the computersystem 1000 by computing systems 1017 and/or by data sources 1019 may bethrough a web-enabled user access point such as the computing systems'1017 or data source's 1019 personal computer, cellular phone,smartphone, laptop, tablet computer, e-reader device, audio player, orother device capable of connecting to the network 1016. Such a devicemay have a browser module that is implemented as a module that usestext, graphics, audio, video, and other media to present data and toallow interaction with data via the network 1016.

The browser module may be implemented as a combination of an all pointsaddressable display such as a cathode-ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. In addition, the browser module may be implemented tocommunicate with input devices 1012 and may also comprise software withthe appropriate interfaces which allow a user to access data through theuse of stylized screen elements such as, for example, menus, windows,dialog boxes, toolbars, and controls (for example, radio buttons, checkboxes, sliding scales, and so forth). Furthermore, the browser modulemay communicate with a set of input and output devices to receivesignals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 1000 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 1000, including the client server systems or the main serversystem, an/or may be operated by one or more of the data sources 1019and/or one or more of the computing systems 1017. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 1017 who are internal to anentity operating the computer system 1000 may access the significantattributes system module 1006 internally as an application or processrun by the CPU 1002.

User Access Point

In an embodiment, a user access point or user interface comprises apersonal computer, a laptop computer, a tablet computer, an e-readerdevice, a cellular phone, a smartphone, a GPS system, a Blackberry®device, a portable computing device, a server, a computer workstation, alocal area network of individual computers, an interactive kiosk, apersonal digital assistant, an interactive wireless communicationsdevice, a handheld computer, an embedded computing device, an audioplayer, or the like.

Other Systems

In addition to the systems that are illustrated in FIG. 10, the network1016 may communicate with other data sources or other computing devices.The computing system 1000 may also comprise one or more internal and/orexternal data sources. In some embodiments, one or more of the datarepositories and the data sources may be implemented using a relationaldatabase, such as DB2, Sybase, Oracle, CodeBase and Microsoft® SQLServer as well as other types of databases such as, for example, a flatfile database, an entity-relationship database, and object-orienteddatabase, and/or a record-based database.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Although this invention has been disclosed in the context of certainpreferred embodiments and examples, it will be understood by thoseskilled in the art that the present invention extends beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses of the invention and obvious modifications and equivalentsthereof. Additionally, the skilled artisan will recognize that any ofthe above-described methods can be carried out using any appropriateapparatus. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an embodiment can be used in all otherembodiments set forth herein. For all of the embodiments describedherein the steps of the methods need not be performed sequentially.Thus, it is intended that the scope of the present invention hereindisclosed should not be limited by the particular disclosed embodimentsdescribed above.

What is claimed is:
 1. A system for monitoring electronic interactionswith unique items and identifying and analyzing significant attributesof the unique items, the system comprising: one or more computerreadable storage devices configured to store a plurality of computerexecutable instructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: generate user activity data by at leastelectronically communicating, over a computer network, with a pluralityof user devices to receive monitoring data related to user interactionswith unique items displayed by user interfaces of the plurality of userdevices, wherein the monitoring data comprises position data related toa position of a unique item displayed by a user interface at a time ofthe user interaction; generate one or more driver models configured toenable identification of which of a plurality of attributes of aselected unique item are driver attributes and to enable determinationof values associated with the driver attributes, the plurality ofattributes comprising at least a condition attribute and a featureattribute, wherein generating the one or more driver models comprises:using the position data to reduce any position bias present in themonitoring data; and using one or more of the following methods: linearregression, non-linear regression, model trees, nearest neighboranalysis; receive selected item data, the selected item data beingrelated to the plurality of attributes of the selected unique item; andapply one or more of the generated driver models to the selected itemdata to generate values associated with driver attributes of theselected unique item for use in generating recommendations ofalternative unique items.
 2. The system of claim 1, wherein the selectedunique item comprises one of the following types of items: usedautomobiles, existing homes, real estate, household goods, customizedelectronics, customized goods.
 3. The system of claim 1, wherein the oneor more driver models comprises at least one price driver model and atleast one demand driver model, the at least one price driver modelconfigured to enable identification of which of the plurality ofattributes of the selected unique item are price driver attributes, theat least one demand driver model configured to enable identification ofwhich of the plurality of attributes of the selected unique item aredemand driver attributes.
 4. The system of claim 1, wherein thegenerated values associated with the driver attributes of the selectedunique item describe at least one of the following: an estimated pricecontribution of a driver attribute to an overall price of the selectedunique item, a perceived value of a driver attribute, a level ofdesirability of a driver attribute.
 5. The system of claim 1, whereinthe one or more hardware computer processors are further configured toexecute the plurality of computer executable instructions in order tocause the system to: electronically present an identification of whichof the plurality of attributes of the selected unique item are driverattributes.
 6. The system of claim 1, wherein the one or more hardwarecomputer processors are further configured to execute the plurality ofcomputer executable instructions in order to cause the system to:electronically present one or more recommendations of alternative uniqueitems.
 7. The system of claim 1, wherein the electronically monitoreduser interactions comprises at least one of the following: a number ofclicks on a hyperlink related to a specific unique item, a number ofviews of a webpage comprising information related to a specific uniqueitem, a position of a specific unique item in a list of a plurality ofspecific unique items when a user interacts with the specific uniqueitem, an amount of time a user spends viewing a webpage comprisinginformation related to a specific unique item, a purchase by a user of aspecific unique item, information submitted by a user to an advertiserrelating to a specific unique item.
 8. A computer-implemented method formonitoring electronic interactions with unique items and identifying andanalyzing significant attributes of the unique items, thecomputer-implemented method comprising: generating, by a computersystem, user activity data by at least electronically communicating,over a computer network, with a plurality of user devices to receivemonitoring data related to user interactions with unique items displayedby user interfaces of the plurality of user devices, wherein themonitoring data comprises position data related to a position of aunique item displayed by a user interface at a time of the userinteraction; generating, by the computer system, one or more drivermodels configured to enable identification of which of a plurality ofattributes of a selected unique item are driver attributes and to enabledetermination of values associated with the driver attributes, theplurality of attributes comprising at least a condition attribute and afeature attribute, wherein generating the one or more driver modelscomprises: using the position data to reduce any position bias presentin the monitoring data; and using one or more of the following methods:linear regression, non-linear regression, model trees, nearest neighboranalysis; receiving, by the computer system, selected item data, theselected item data being related to the plurality of attributes of theselected unique item; and applying, by the computer system, one or moreof the generated driver models to the selected item data to generatevalues associated with driver attributes of the selected unique item foruse in generating recommendations of alternative unique items, whereinthe computer system comprises a computer processor and electronicmemory.
 9. The computer-implemented method of claim 8, wherein theselected unique item comprises one of the following types of items: usedautomobiles, existing homes, real estate, household goods, customizedelectronics, customized goods.
 10. The computer-implemented method ofclaim 8, wherein the one or more driver models comprises at least oneprice driver model and at least one demand driver model, the at leastone price driver model configured to enable identification of which ofthe plurality of attributes of the selected unique item are price driverattributes, the at least one demand driver model configured to enableidentification of which of the plurality of attributes of the selectedunique item are demand driver attributes.
 11. The computer-implementedmethod of claim 8, wherein the generated values associated with thedriver attributes of the selected unique item describe at least one ofthe following: an estimated price contribution of a driver attribute toan overall price of the selected unique item, a perceived value of adriver attribute, a level of desirability of a driver attribute.
 12. Thecomputer-implemented method of claim 8, further comprising:electronically presenting an identification of which of the plurality ofattributes of the selected unique item are driver attributes.
 13. Thecomputer-implemented method of claim 8, further comprising:electronically presenting one or more recommendations of alternativeunique items.
 14. The computer-implemented method of claim 8, whereinthe electronically monitored user interactions comprises at least one ofthe following: a number of clicks on a hyperlink related to a specificunique item, a number of views of a webpage comprising informationrelated to a specific unique item, a position of a specific unique itemin a list of a plurality of specific unique items when a user interactswith the specific unique item, an amount of time a user spends viewing awebpage comprising information related to a specific unique item, apurchase by a user of a specific unique item, information submitted by auser to an advertiser relating to a specific unique item.
 15. A computerreadable, non-transitory storage medium having a computer program storedthereon for causing a suitably programmed computer system to process byone or more processors computer-program code by performing a method formonitoring electronic interactions with unique items and identifying andanalyzing significant attributes of the unique items when the computerprogram is executed on the suitably programmed computer system, themethod comprising: generating, by a computer system, user activity databy at least electronically communicating, over a computer network, witha plurality of user devices to receive monitoring data related to userinteractions with unique items displayed by user interfaces of theplurality of user devices, wherein the monitoring data comprisesposition data related to a position of a unique item displayed by a userinterface at a time of the user interaction; generating, by the computersystem, one or more driver models configured to enable identification ofwhich of a plurality of attributes of a selected unique item are driverattributes and to enable determination of values associated with thedriver attributes, the plurality of attributes comprising at least acondition attribute and a feature attribute, wherein generating the oneor more driver models comprises: using the position data to reduce anyposition bias present in the monitoring data; and using one or more ofthe following methods: linear regression, non-linear regression, modeltrees, nearest neighbor analysis; receiving, by the computer system,selected item data, the selected item data being related to theplurality of attributes of the selected unique item; and applying, bythe computer system, one or more of the generated driver models to theselected item data to generate values associated with driver attributesof the selected unique item for use in generating recommendations ofalternative unique items, wherein the computer system comprises acomputer processor and electronic memory.
 16. The computer readable,non-transitory storage medium of claim 15, wherein the selected uniqueitem comprises one of the following types of items: used automobiles,existing homes, real estate, household goods, customized electronics,customized goods.
 17. The computer readable, non-transitory storagemedium of claim 15, wherein the one or more driver models comprises atleast one price driver model and at least one demand driver model, theat least one price driver model configured to enable identification ofwhich of the plurality of attributes of the selected unique item areprice driver attributes, the at least one demand driver model configuredto enable identification of which of the plurality of attributes of theselected unique item are demand driver attributes.
 18. The computerreadable, non-transitory storage medium of claim 15, wherein thegenerated values associated with the driver attributes of the selectedunique item describe at least one of the following: an estimated pricecontribution of a driver attribute to an overall price of the selectedunique item, a perceived value of a driver attribute, a level ofdesirability of a driver attribute.
 19. The computer readable,non-transitory storage medium of claim 15, the method furthercomprising: electronically presenting an identification of which of theplurality of attributes of the selected unique item are driverattributes.
 20. The computer readable, non-transitory storage medium ofclaim 15, the method further comprising: electronically presenting oneor more recommendations of alternative unique items.